Forecasting the cross-sectional stock returns: Evidence from the United Kingdom
Bibliographic record
Abstract
The study provides the forecasts of expected returns based on cross-sectional estimates from the Fama-Macbeth regressions in the United Kingdom. We collected the data of listed firms on the London Stock Exchange on the DataStream from January 1980 to December 2020. We analyze the data sample by employing three cross-sectional models' ten-year rolling estimates of Fama-Macbeth slopes. The empirical findings demonstrate that an investor can derive a composite estimate of the expected return by integrating various company-specific variables in real-time. Model 1 indicates that the expected-return estimates have a predictive slope for future monthly returns of 95.07%, with a standard error of 0.1981. Moreover, model 2 and model 3 report the predictability of returns are 77.57% and 76.94%. In short, our empirical evidence suggests that investors and stakeholders may consider using model 1 to estimate the cost of equity due to its simplicity and effective prediction capability. Our findings are consistent with trade-off theory and prior literature.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".